Scheduling under Uncertainty: Planning for the Ubiquitous Grid

Sample, Neal; Keyani, Pedram; Wiederhold, Gio

doi:10.1007/3-540-46000-4_28

Cited by 18 publications

(7 citation statements)

References 14 publications

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“…The data gatherer evaluates current and future CPU availability by connecting to an external Network Weather Service (NWS) [50], if present, or directly employing the hosts' descriptions. To assess network performances the scheduler adopts the NWS as well, if present, or a simple probing technique such as those in [42]. Finally, computational costs are computed as compTime(ds, s, p, h, t) = req (ds, s, p) per f (h)·avail(h, t) , where req(ds, s, p) represents the processing requirements (with respect to a reference host) of software s run with parameters p on datasets ds, per f (h) is the no-load performance of host h, and avail(h, t) is the fraction of processing cycles available on host h at time t; this process is based on task profiling and analytical benchmarking [21,28,39].…”

Section: Example Implementation Of An Application-oriented Scheduler mentioning

confidence: 99%

“…Service level agreements (SLAs) are typically considered as suitable means to support the necessary reliability. Thus, resource reservations and qualityof-service guarantees are being pointed out as a major requirement of modern GRM systems [36,38]; however, even in the absence of SLAs, some other approaches have been proposed to improve reliability and performance of resources including, e.g., performance prediction, job replication, and dynamic adaption to performance changes [35,42,50].…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Supporting Grid Scheduling

2007

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Grid resource management systems and schedulers are important components for building Grids. They are responsible for the selection and allocation of Grid resources to current and future applications. Thus, they are important building blocks for making Grids available to user communities. In this paper we briefly analyze the requirements of Grid resource management and provide a classification of schedulers. Then, we define an extensible formal model for Grid scheduling activities, and characterize the general Grid scheduling problem. Finally, we provide a reference architecture for the support of our model and discuss different aspects of architectural implementations.

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Section: Example Implementation Of An Application-oriented Scheduler mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Supporting Grid Scheduling

2007

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“…For instance, one can assume each node with a local selection strategy designed to achieve an overall objective, e.g., to balance out the load through the Grid and at the same time taking care of the dynamic characteristics of the individual nodes adaptively (Wang and Liu 2003;Cheung et al 2004a,b). Or one can also consider the selection of all the services within one flow as a whole so as to provide more robust and fault-tolerance service selection to meet some QoS promise for each flow execution (Mao, Brewer, and Katz 2001;Sample, Keyani, and Wiederhold 2002;Pistore, Bertoli, and Traverso 2004). Also, the undeterministic conditions of individual services can also be modeled using probabilistic models, e.g., Markov Decision Process (Doshi et al 2004), for computing the optimal policy of each state of a service flow, where the state of a flow changes when an action (service) is successfully taken (invoked) or not.…”

Section: Service Flow Scheduling and Recoverymentioning

confidence: 99%

On Knowledge Grid and Grid Intelligence: A Survey

Cheung¹,

Liu²

2005

Computational Intelligence

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The next generation Web Intelligence (WI) aims at enabling users to go beyond the existing online information search and knowledge queries functionalities and to gain, from the Web, 1 practical wisdom for problem solving. To support such a Wisdom Web, we envision that a grid-like computing infrastructure with intelligent service agencies is needed, where these agencies can interact, self-organize, learn, and evolve their course of actions, identities, and interrelationships for new knowledge creation, as well as scientific and social evolution. In this paper, we first provide an overview of recent development in WI and Semantic/Knowledge Grid. Then, the fundamental capabilities of the Wisdom Web as well as the conceptual architecture of an intelligent Grid for supporting it are described. Technical challenges for realizing Grid Intelligence are highlighted and the recent advancements in related research areas are reviewed.

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“…Asynchronous execution of web services is achieved by using queues for event processing. The default queuing protocol in FICAS is FIFO (first in and first out), so event messages are processed in the order they arrive, but other methods are being developed [20].…”

Section: Software Service Modelmentioning

confidence: 99%

Efficient integration of web services with distributed data flow and active mediation

Liu

Peng

Law

et al. 2004

Proceedings of the 6th International Conference on Electronic Commerce - ICEC '04

Self Cite

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This paper presents a loosely coupled service-composition paradigm. This paradigm employs a distributed data flow that differs markedly from centralized information flow adopted by current service integration frameworks, such as CORBA, J2EE and SOAP. Distributed data flows support direct data transmission to avoid many performance bottlenecks of centralized processing. In addition, active mediation is used in applications employing multiple web services that are not fully compatible in terms of data formats and contents. Active mediation increases the applicability of the services, reduces data communication among the services, and enables the application to control complex computations. The benefits of distributed data flow and active mediation are illustrated with various applications, such as dynamic type conversion, result extraction, and engineering application. It is shown that active mediation, combining with distributed data flows, can greatly improve the performance of an application utilizing multiple web services.

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Scheduling under Uncertainty: Planning for the Ubiquitous Grid

Cited by 18 publications

References 14 publications

Modeling and Supporting Grid Scheduling

Modeling and Supporting Grid Scheduling

On Knowledge Grid and Grid Intelligence: A Survey

Efficient integration of web services with distributed data flow and active mediation

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